Source Distance Estimation in Turbulent Airflow: Exploiting Molecule Degradation Diversity

TL;DR

This paper demonstrates that molecule degradation diversity in mixtures can improve source distance estimation in turbulent airflow, enhancing molecular communication applications.

Contribution

It introduces a low-complexity method leveraging degradation rate differences in molecule mixtures for accurate source localization.

Findings

Diversity in molecule degradation rates aids source distance estimation.

Combining degradation features with concentration and temporal data improves accuracy.

Simulation results confirm the effectiveness of the proposed approach.

Abstract

In nature, estimating the location of a molecule source in turbulent airflow is a central, and yet highly challenging problem for mate search and foraging. Recently, it has also received increasing attention in synthetic molecular communication (SMC), e.g., for leakage detection. One important aspect of source localization is to estimate the distance to the molecule source, e.g., to determine whether it is worth to travel to a potential mating partner or food source, or to decide whether a leak is close enough for inspection. In this study, based on realistic simulations, we show that the diversity induced by molecule mixtures can aid source localization. In particular, when different molecule types in a mixture are subject to atmospheric degradation with different degradation rates, the relative abundance of the different species observed at the receiver enables low-complexity estimation of the source distance. Furthermore, this feature can be combined with already established concentration-based and temporal features of observed molecular signals to further increase estimation accuracy. Thereby, we show that molecule degradation diversity of molecule mixtures can help to realize one of the important envisioned SMC applications, namely source localization, even in turbulent airflow, opening new opportunities for the exploitation of SMC to solve real-world problems.